In this work, we propose to use a deep learning model with relational inductive bias [1]} for the task of one-shot classification.
The proposed model uses self-attention and cross-attention blocks of the Transformer [2]} to compare a pair of images. Relational models with Transformer-based attention have recently been shown to succeed in vision tasks [3]}, [4]}, [5]}
and in this paper we show that this type of models can perform well in few-shot learning. Our proposed model is to the best of our knowledge the first deep learning model that achieves perfect (and better than human) accuracy in the most challenging Omniglot classification setup: one-shot within alphabet classification with 20 alternative classes without using data augmentation.
<FIGURE>